Video experts continually seek new algorithms and methods for improving the quality of video images. The primary goal is to obtain the most perceptually appealing video image possible. The ultimate criterion is the question “How well does the viewer like the resulting picture?” One way to answer the question is to have a panel of viewers watch certain video sequences and then record the opinions of the viewers concerning the resulting image quality. The results, however, will vary from panel to panel according to the variability between the viewing panels. This problem is commonly encountered when relying on subjective human opinion. The severity of the problem is increased when the viewing panel is composed of non-experts.
Results solely based upon on human perception and subjective opinion are usually subjected to subsequent statistical analysis to remove ambiguities that result from the non-deterministic nature of subjective results. Linear and non-linear heuristic statistical models have been proposed to normalize these types of subjective results and obtain certain figures of merit that represent the goodness (or the degradation) of video quality. The process of measuring video quality in this manner is referred to as “subjective video quality assessment.”
Subjective video quality assessment methods give valid indications of visible video artifacts. Subjective video quality assessment methods, however, are probabilistic in nature, complex, time consuming, and sometimes difficult to apply. In addition, there is a problem in selecting appropriate viewers for the viewing panel. A non-trained viewer will be a poor judge of the suitability of new video processing methods. A non-trained viewer, however, will likely accurately represent the general consumers in the marketplace. On the other hand, a trained expert viewer will be overly biased toward detecting minor defects that will never be noticed by the general consumer.
To avoid the disadvantages that attend subjective methods for evaluating video quality, it is desirable to use automated objective methods to evaluate video quality. Automated objective methods seek to obtain objective figure of merits to quantify the goodness (or the degradation) of video quality. The process for obtaining one or more objective measures of the video quality must be automated in order to be able to quickly analyze differing types of video algorithms as the video algorithms sequentially appear in a video stream.
Objective measures of video quality are fully deterministic. That is, the results will always be the same when the test is repeated (assuming the same settings are preserved).
Because the ultimate goal is to present the viewer with the most appealing picture, a final judge of the value of the objective measures of video quality is the degree of correlation that the objective measures have with the subjective results. Statistical analysis is usually used to correlate the results objectively obtained (automatically generated) with the results subjectively obtained (from human opinion).
There is a need in the art for improved systems and methods for automatically measuring video quality. The process of automatically measuring video quality is referred to as “objective video quality assessment.”
Several different types of algorithms have been proposed that are capable of providing objective video quality assessment. The algorithms are generally referred to as “objective video quality models.” A report from the Video Quality Experts Group (VQEG) sets forth and describes the results of an evaluation performed on ten (10) objective video quality models. The report is dated December 1999 and is entitled “Final Report from the Video Quality Experts Group on the validation of Objective Models of Video Quality Assessment.” The report is presently available on the World Wide Web at http://www-ext.crc.ca/VQEG.
Each different objective video quality model provides its own distinctive measurement of video quality referred to as an “objective metric.” A “double ended” objective metric is one that evaluates video quality using a first original video image and a second processed video image. A “double ended” objective metric compares the first original video image to the second processed video image to evaluate video quality by determining changes in the original video image. A “single ended” objective metric is one that evaluates video quality without referring to the original video image. A “single ended” objective metric applies an algorithm to a video image to evaluate its quality.
No single objective metric has been found to be superior to all the other objective metrics under all conditions and for all video artifacts. Each objective metric has its own advantages and disadvantages. Objective metrics differ widely in performance (i.e., how well their results correlate with subjective quality assessment results), and in stability (i.e., how well they handle different types of video artifacts), and in complexity (i.e., how much computation power is needed to perform the algorithm calculations).
A wide range of applications exists to which objective metrics may be applied. For example, fast real-time objective metrics are needed to judge the quality of a broadcast video signal. On the other hand, more complex and reliable objective metrics are better for judging the quality of non-real time video simulations.
Using only one objective metric (and one objective video quality model) limits the evaluation of the quality of a video signal to the level of evaluation that is obtainable from the objective metric that is used. It is therefore desirable to use more than one objective metric for video quality evaluation. An improved system and method that uses more than one objective metric for video quality evaluation has been disclosed in U.S. patent application Ser. No. 09/734,823 filed Dec. 12, 2000 by Ali et al. entitled “System and Method for Providing a Scalable Dynamic Objective Metric for Automatic Video Quality Evaluation.”
There is a need in the art for an improved system and method for combining objective metrics in order to form more efficient objective metrics for video quality evaluation.